Techniques for behavioral pairing model evaluation in a contact center system

ABSTRACT

Techniques for behavioral pairing model evaluation in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for behavioral pairing model evaluation in a contact center system comprising determining an ordering of a plurality of agents, determining an ordering of a plurality of contact types; analyzing, historical contact-agent outcome data according to the orderings of the pluralities of agents and contact types to construct a pairing model; and determining an expected performance of the contact center system using the pairing model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 15/785,933, filed Oct. 17, 2017, now U.S. Pat. No. 10,348,900, issued Jul. 9, 2019, which is a continuation of U.S. patent application Ser. No. 15/377,397, filed Dec. 13, 2016, now U.S. Pat. No. 9,888,121, issued Feb. 6, 2018, each of which is hereby incorporated by reference in its entirety as if fully set forth herein.

FIELD OF THE DISCLOSURE

This disclosure generally relates to model evaluation for pairing contacts and agents in contact centers and, more particularly, to techniques for behavioral pairing model evaluation in a contact center system.

BACKGROUND OF THE DISCLOSURE

A typical contact center algorithmically assigns contacts arriving at the contact center to agents available to handle those contacts. At times, the contact center may have agents available and waiting for assignment to inbound or outbound contacts (e.g., telephone calls, Internet chat sessions, email). At other times, the contact center may have contacts waiting in one or more queues for an agent to become available for assignment.

In some typical contact centers, contacts are assigned to agents ordered based on time of arrival, and agents receive contacts ordered based on the time when those agents became available. This strategy may be referred to as a “first-in, first-out”, “FIFO”, or “round-robin” strategy. In other typical contact centers, other strategies may be used, such as “performance-based routing”, or a “PBR” strategy.

In other, more advanced contact centers, contacts are paired with agents using a “behavioral pairing”, or a “BP” strategy, under which contacts and agents may be deliberately (preferentially) paired in a fashion that enables the assignment of subsequent contact-agent pairs such that when the benefits of all the assignments under a BP strategy are totaled they may exceed those of FIFO and other strategies such as performance-based routing (“PBR”) strategies. BP is designed to encourage balanced utilization of agents within a skill queue while nevertheless simultaneously improving overall contact center performance beyond what FIFO or PBR methods will allow. This is a remarkable achievement inasmuch as BP acts on the same calls and same agents as FIFO or PBR methods, utilizes agents approximately evenly as FIFO provides, and yet improves overall contact center performance. BP is described in, e.g., U.S. Pat. No. 9,300,802, which is incorporated by reference herein. Additional information about these and other features regarding the pairing or matching modules (sometimes also referred to as “SATMAP”, “routing system”, “routing engine”, etc.) is described in, for example, U.S. Pat. No. 8,879,715, which is incorporated herein by reference.

A BP strategy may develop a model of agents, or agent groups and contact types, from which expected gains over other pairing strategies may be determined. However, there are currently no techniques for improving model generation and validation to optimize expected gains.

In view of the foregoing, it may be understood that there is a need for a system that enables improving behavioral pairing model selection to improve the efficiency and performance of pairing strategies that are designed to choose among multiple possible pairings.

SUMMARY OF THE DISCLOSURE

Techniques for behavioral pairing model evaluation in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for behavioral pairing model evaluation in a contact center system comprising determining an ordering of a plurality of agents, determining an ordering of a plurality of contact types; analyzing, historical contact-agent outcome data according to the orderings of the pluralities of agents and contact types to construct a pairing model; and determining an expected performance of the contact center system using the pairing model.

In accordance with other aspects of this particular embodiment, a behavioral pairing correction factor may be applied to the pairing model prior to determining the expected performance.

In accordance with other aspects of this particular embodiment, the pairing model may be a behavioral pairing model and/or based on a diagonal pairing strategy.

In accordance with other aspects of this particular embodiment, a second expected performance of the contact center system may be determined using a FIFO pairing strategy, and an expected gain of the contact center system may be determined using the pairing model instead of the FIFO pairing strategy.

In accordance with other aspects of this particular embodiment, a second pairing model may be constructed based at least on a second ordering of a second plurality of contact types different from the first plurality of contact types, a second expected performance of the contact center system may be determined using the second pairing model, the second expected performance based on the second pairing model may be compared with the expected performance based on the pairing model, and one of at least the pairing model and the second pairing model may be selected based on the comparing of the expected performance and the second expected performance.

In accordance with other aspects of this particular embodiment, new contact-agent outcome data may be determined, the pairing model may be updated based on the new contact-agent outcome data, and an updated expected performance of the contact center system may be determined using the updated pairing model.

In another particular embodiment, the techniques may be realized as a system for behavioral pairing model evaluation in a contact center system comprising at least one computer processor configured to operate in the contact center system, wherein the at least one computer processor is configured to perform the steps in the above-discussed method.

In another particular embodiment, the techniques may be realized as an article of manufacture for behavioral pairing model evaluation in a contact center system comprising a non-transitory processor readable medium and instructions stored on the medium, wherein the instructions are configured to be readable from the medium by at least one computer processor configured to operate in the contact center system and thereby cause the at least one computer processor to operate to perform the steps in the above-discussed method.

The present disclosure will now be described in more detail with reference to particular embodiments thereof as shown in the accompanying drawings. While the present disclosure is described below with reference to particular embodiments, it should be understood that the present disclosure is not limited thereto. Those of ordinary skill in the art having access to the teachings herein will recognize additional implementations, modifications, and embodiments, as well as other fields of use, which are within the scope of the present disclosure as described herein, and with respect to which the present disclosure may be of significant utility.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to facilitate a fuller understanding of the present disclosure, reference is now made to the accompanying drawings, in which like elements are referenced with like numerals. These drawings should not be construed as limiting the present disclosure, but are intended to be illustrative only.

FIG. 1 shows a block diagram of a contact center according to embodiments of the present disclosure.

FIG. 2 depicts a schematic representation of a BP model according to embodiments of the present disclosure.

FIG. 3 depicts a schematic representation of a BP model according to embodiments of the present disclosure.

FIG. 4 shows a flow diagram of a BP model evaluation method according to embodiments of the present disclosure.

DETAILED DESCRIPTION

A typical contact center algorithmically assigns contacts arriving at the contact center to agents available to handle those contacts. At times, the contact center may have agents available and waiting for assignment to inbound or outbound contacts (e.g., telephone calls, Internet chat sessions, email) or outbound contacts. At other times, the contact center may have contacts waiting in one or more queues for an agent to become available for assignment.

In some typical contact centers, contacts are assigned to agents ordered based on time of arrival, and agents receive contacts ordered based on the time when those agents became available. This strategy may be referred to as a “first-in, first-out”, “FIFO”, or “round-robin” strategy. In other typical contact centers, other strategies may be used, such as “performance-based routing”, or a “PBR” strategy.

In other, more advanced contact centers, contacts are paired with agents using a “behavioral pairing”, or a “BP” strategy, under which contacts and agents may be deliberately (preferentially) paired in a fashion that enables the assignment of subsequent contact-agent pairs such that when the benefits of all the assignments under a BP strategy are totaled they may exceed those of FIFO and other strategies such as performance-based routing (“PBR”) strategies. BP is designed to encourage balanced utilization of agents within a skill queue while nevertheless simultaneously improving overall contact center performance beyond what FIFO or PBR methods will allow. This is a remarkable achievement inasmuch as BP acts on the same calls and same agents as FIFO or PBR methods, utilizes agents approximately evenly as FIFO provides, and yet improves overall contact center performance. BP is described in, e.g., U.S. Pat. No. 9,300,802, which is incorporated by reference herein. Additional information about these and other features regarding the pairing or matching modules (sometimes also referred to as “SATMAP”, “routing system”, “routing engine”, etc.) is described in, for example, U.S. Pat. No. 8,879,715, which is incorporated herein by reference.

In some embodiments, a contact center may switch (or “cycle”) periodically among at least two different pairing strategies (e.g., between FIFO and a BP strategy). Additionally, the outcome of each contact-agent interaction may be recorded along with an identification of which pairing strategy (e.g., FIFO, PBR, or BP) had been used to assign that particular contact-agent pair. By tracking which interactions produced which results, the contact center may measure the performance attributable to a first strategy (e.g., FIFO) and the performance attributable to a second strategy (e.g., BP). In this way, the relative performance of one strategy may be benchmarked against the other. The contact center may, over many periods of switching between different pairing strategies, more reliably attribute performance gain to one strategy or the other. Benchmarking pairing strategies is described in, e.g., U.S. patent application Ser. No. 15/131,915, filed Apr. 18, 2016, which is incorporated herein by reference.

A BP strategy may develop a model of agents or agent groups and contact types, from which expected gain over other pairing strategies may be determined. Therefore, techniques for improved model generation and validation are desirable to optimize the expected gain.

In view of the foregoing, it may be understood that there is a need for a system that enables improving behavioral pairing model selection to improve the efficiency and performance of pairing strategies that are designed to choose among multiple possible pairings.

FIG. 1 shows a block diagram of a contact center system 100 according to embodiments of the present disclosure. The description herein describes network elements, computers, and/or components of a system and method for simulating contact center systems that may include one or more modules. As used herein, the term “module” may be understood to refer to computing software, firmware, hardware, and/or various combinations thereof. Modules, however, are not to be interpreted as software which is not implemented on hardware, firmware, or recorded on a processor readable recordable storage medium (i.e., modules are not software per se). It is noted that the modules are exemplary. The modules may be combined, integrated, separated, and/or duplicated to support various applications. Also, a function described herein as being performed at a particular module may be performed at one or more other modules and/or by one or more other devices instead of or in addition to the function performed at the particular module. Further, the modules may be implemented across multiple devices and/or other components local or remote to one another. Additionally, the modules may be moved from one device and added to another device, and/or may be included in both devices.

As shown in FIG. 1, the contact center system 100 may include a central switch 110. The central switch 110 may receive incoming contacts (e.g., callers) or support outbound connections to contacts via a telecommunications network (not shown). The central switch 110 may include contact routing hardware and software for helping to route contacts among one or more contact centers, or to one or more PBX/ACDs or other queuing or switching components, including other Internet-based, cloud-based, or otherwise networked contact-agent hardware or software-based contact center solutions.

The central switch 110 may not be necessary such as if there is only one contact center, or if there is only one PBX/ACD routing component, in the contact center system 100. If more than one contact center is part of the contact center system 100, each contact center may include at least one contact center switch (e.g., contact center switches 120A and 120B). The contact center switches 120A and 120B may be communicatively coupled to the central switch 110. In embodiments, various topologies of routing and network components may be configured to implement the contact center system.

Each contact center switch for each contact center may be communicatively coupled to a plurality (or “pool”) of agents. Each contact center switch may support a certain number of agents (or “seats”) to be logged in at one time. At any given time, a logged-in agent may be available and waiting to be connected to a contact, or the logged-in agent may be unavailable for any of a number of reasons, such as being connected to another contact, performing certain post-call functions such as logging information about the call, or taking a break.

In the example of FIG. 1, the central switch 110 routes contacts to one of two contact centers via contact center switch 120A and contact center switch 120B, respectively. Each of the contact center switches 120A and 120B are shown with two agents each. Agents 130A and 130B may be logged into contact center switch 120A, and agents 130C and 130D may be logged into contact center switch 120B.

The contact center system 100 may also be communicatively coupled to an integrated service from, for example, a third party vendor. In the example of FIG. 1, pairing model evaluation module 140 may be communicatively coupled to one or more switches in the switch system of the contact center system 100, such as central switch 110, contact center switch 120A, or contact center switch 120B. In some embodiments, switches of the contact center system 100 may be communicatively coupled to multiple pairing model evaluation modules (e.g., a BP model evaluation module). In some embodiments, pairing model evaluation module 140 may be embedded within a component of a contact center system (e.g., embedded in or otherwise integrated with a switch). The pairing model evaluation module 140 may receive information from a switch (e.g., contact center switch 120A) about agents logged into the switch (e.g., agents 130A and 130B) and about incoming contacts via another switch (e.g., central switch 110) or, in some embodiments, from a network (e.g., the Internet or a telecommunications network) (not shown).

A contact center may include multiple pairing modules (e.g., a BP module and a FIFO module) (not shown), and one or more pairing modules may be provided by one or more different vendors. In some embodiments, one or more pairing modules may be components of pairing model evaluation module 140 or one or more switches such as central switch 110 or contact center switches 120A and 120B. In some embodiments, a pairing model evaluation module may determine which pairing module may handle pairing for a particular contact. For example, the pairing model evaluation module may alternate between enabling pairing via the BP module and enabling pairing with the FIFO module. In other embodiments, one pairing module (e.g., the BP module) may be configured to emulate other pairing strategies. For example, a pairing model evaluation module, or a pairing model evaluation component integrated with BP components in the BP module, may determine whether the BP module may use BP pairing or emulated FIFO pairing for a particular contact. In this case, “BP on” may refer to times when the BP module is applying the BP pairing strategy, and “BP off” may refer to other times when the BP module is applying a different pairing strategy (e.g., FIFO).

In some embodiments, regardless of whether pairing strategies are handled by separate modules, or if some pairing strategies are emulated within a single pairing module, the single pairing module may be configured to monitor and store information about pairings made under any or all pairing strategies. For example, a BP module may observe and record data about FIFO pairings made by a FIFO module, or the BP module may observe and record data about emulated FIFO pairings made by a BP module operating in FIFO emulation mode.

FIG. 2 depicts a schematic representation of a BP model 200 according to embodiments of the present disclosure. BP model 200 is a simple 2×2 model with an ordering of two groups of agents (Agent Group A and Agent Group B) and an ordering of two types of contacts (Contact Type A and Contact Type B). In real-world contact centers, there may be dozens, hundreds, or more agents or groups ordered in a model, and there may also be many more types of contacts ordered in a model.

In BP model 200, there are four pairwise possibilities: a contact of Contact Type A is paired with an agent of Agent Group A (pairing 201); a contact of Contact Type A is paired with an agent of Agent Group B (pairing 202); a contact of Contact Type B is paired with an agent of Agent Group A (pairing 203); and a contact of Contact Type B is paired with an agent of Agent Group B (pairing 204).

In the hypothetical of BP model 200, a review of historical contact outcome data shows the following: pairing 201 shows a volume of 10 contacts and an average revenue of $5 per contact; pairing 202 shows a volume of 10 contacts and an average revenue of $25 per contact; pairing 203 shows a volume of 25 contacts and an average revenue of $10 per contact; and pairing 204 shows a volume of 40 contacts and an average revenue of $20 per contact.

One technique for computing the expected average revenue per contact across all contact-agent pairings is to compute the weighted average across all possible pairings as shown below in Equation 1: (25·10+40·20+25·10+10·5)/(25+40+25+10)=13.5  (Eq. 1) Thus, the expected revenue per contact in a typical FIFO pairing is $13.50 per contact.

In some embodiments of a BP strategy, the contact center system (via, e.g., a BP component or module embedded or communicatively coupled to the contact center system) may preferably pair contacts to agents along a diagonal of the model (e.g., diagonal 210). In the example of BP model 200, Contact Type A may be preferably paired to Agent Group A, and Contact Type B may be preferably paired to Agent Group B given optimal availability of choice.

One technique for estimating the expected revenue per contact under a BP strategy is to compute the weighted average across all preferred pairings as shown below in Equation 2: (40·20+10·5)/(40+10)=17  (Eq. 2) Thus, the expected revenue per contact appears to be $17 per contact, and the expected gain or improvement over a FIFO pairing strategy is $3.50 per contact, or a gain of almost 26% over FIFO.

The computation shown in Eq. 2 implicitly assumes that calls paired using a BP strategy will be distributed uniformly throughout the pairings 201 and 204, and it computes the weighted proportion of pairings falling uniformly in a square grid with area 10 of pairing 201 to the proportion of pairings falling uniformly in a square grid of area 40 of pairing 204.

However, in practice, the BP strategy is likely to hew more closely to the diagonal with respect to the distance or Z-score that a particular pairing of an ordered contact and an ordered agent will fall from the diagonal. A more accurate estimate of expected gain for BP over FIFO may account for a narrower set of contact-agent pairings along the diagonal, computing instead the proportion of pairings falling uniformly along or near the diagonal through each pairing. In some embodiments, a weighted average according to the proportional length of the diagonal through each preferred pairing under a BP strategy may be computed. Thus, in some embodiments, the adjusted expected revenue per contact is only $15 per contact, and the adjusted expected gain or improvement over a FIFO pairing strategy is only $1.50 per contact, or a gain of about 11%.

In practice, the real-world gain measured using BP model 200 is more likely to be 11% than 26%. Consequently, it may be advantageous to determine the “adjusted diagonal” rather than the “unadjusted diagonal” of Equation 2. The ability to evaluate expected values and gain of BP models has many benefits, including more accurate revenue/cost-saving forecasting, and improved ability to select optimal models. For example, given a choice between two possible models of ordering contact types and agents (e.g., “Model A” and “Model B”), Model A could show a higher gain than Model B using the unadjusted diagonal computation, whereas Model B could show a higher gain than Model A using the adjusted diagonal computation. In this case, it would be preferable to apply Model B for BP in the contact center system to maximize the real-world expected gain.

In some situations, a model might appear to have a positive expected gain using the unadjusted diagonal computation, but will actually have a negative expected gain (i.e., a loss) using the adjusted diagonal computation. An example of such a model is described below with reference to FIG. 3.

FIG. 3 depicts a schematic representation of a BP model 300 according to embodiments of the present disclosure. Similar to BP model 200 (FIG. 2), BP model 300 is a simple, hypothetical 2×2 model with an ordering of two groups of agents (Agent Group A and Agent Group B) and an ordering of two types of contacts (Contact Type A and Contact Type B).

In BP model 300, there are, again, four pairwise possibilities: a contact of Contact Type A is paired with an agent of Agent Group A (pairing 301); a contact of Contact Type A is paired with an agent of Agent Group B (pairing 302); a contact of Contact Type B is paired with an agent of Agent Group A (pairing 303); and a contact of Contact Type B is paired with an agent of Agent Group B (pairing 304).

In some embodiments of a BP strategy, the contact center system (via, e.g., a BP component or module embedded or communicatively coupled to the contact center system) may preferably pair contacts to agents along a diagonal of the model (e.g., diagonal 310). In the example of BP model 300, Contact Type A may be preferably paired to Agent Group A, and Contact Type B may be preferably paired to Agent Group B given optimal availability of choice.

In the hypothetical of BP model 300, a review of historical contact outcome data shows the following: pairing 301 shows a volume of 21,000 contacts and an average handle time (“AHT”) of 900 seconds per contact; pairing 302 shows a volume of 23,000 contacts and an AHT of 850 seconds per contact; pairing 303 shows a volume of 25,000 contacts and an AHT of 700 seconds per contact; and pairing 304 shows a volume of 26,000 contacts and an average revenue of 650 seconds per contact. Notably, an effective behavioral pairing model for AHT should result in a reduction in AHT (i.e., a lower expected AHT indicates a positive expected gain).

Equations 3 and 4 below compute the baseline FIFO/random expected performance, the unadjusted BP expected performance, and the adjusted diagonal BP expected performance respectively: (21,000·900+23,000·850+25,000·700+26,000·650)/(21,000+23,000+25,000+26,000)≈767  (Eq. 3) (21,000·900+26,000·650)/(21,000+26,000)≈762  (Eq. 4) Thus, the apparent, unadjusted expected performance of BP model 300 is approximately a 5 second per contact reduction in AHT (767 seconds per contact from Equation 3 less 762 seconds per contact from Equation 4), or a 0.7% gain over FIFO pairing. However, the real-world, adjusted expected performance of BP model 300 according to some embodiments is approximately a 1 second per contact increase in AHT (767 seconds per contact from Equation 3 less 768 seconds per contact from an adjusted diagonal computation), or a −0.1% gain compared to FIFO pairing.

In the example of hypothetical BP model 300, a contact center system may have naïvely selected BP model 300 as a viable model to obtain a 0.7% gain. However, based on the adjustment, the contact center system may avoid using BP model 300 because it is expected to decrease overall performance of the contact center system.

FIG. 4 shows a flow diagram of a BP model evaluation method 400 according to embodiments of the present disclosure. At block 410, the BP model evaluation method 400 may begin.

At block 410, an ordering of agents (or groups of agents) may be determined, and, at block 420, an ordering of contact types may be determined. A BP module or similar component may assist with defining contact types and/or agent groups based on a variety of variables (e.g., demographic, psychographic). Orderings of contact types and agents or agent groups may be based on a variety of performance metrics or other metrics (sales, AHT, influencability, etc.).

At block 430, historical contact-agent outcome data may be analyzed according to the agent and contact type orderings determined at blocks 410 and 420. For example, consider a historical pairing between Agent Alice and Contact Bob. An analysis of agent data for Agent Alice determines that Agent Alice would be considered a member of Agent Group A under the BP model determined at blocks 410 and 420, and an analysis of contact data for Contact Bob determines that Contact Bob would be considered a member of Contact Type B under the BP model. The relevant outcome of this contact-agent pairing would be credited to the pairwise pairing group of Agent Group A and Contact Type B. For example, if the contact center seeks to optimize sales, the pairing model evaluation module may note that one of the contacts in this pairing group resulted in a certain amount of revenue (e.g., $0, $10, $100).

The historical outcome data may include a small volume of outcome data, a large volume of outcome data, a threshold amount of outcome data determined to be statistically significant, etc. In some embodiments, outcome data may be limited to a rolling historical window (e.g., 10 days, 30 days, 90 days, 1 year, etc.). In some embodiments, outcome data may be limited to outcomes collected during “off” cycles when FIFO pairing may be in use. In other embodiments, outcome data may include random groupings of “on” (e.g., BP) and/or “off” (e.g., FIFO) pairings.

After the historical outcome data has been analyzed, the resulting BP model may be similar to BP model 200 (FIG. 2) or BP model 300 (FIG. 3), insofar as a grid of pairwise pairing groups for ordered agents or agent groups and contact types may indicate a contact volume and average outcome for each pairing group.

At block 440, a BP correction factor may be applied to the model. For example, as shown with respect to BP models 200 and 300 (FIGS. 2 and 3), a behavioral pairing adjustment may be applied to compute the weighted average of contacts along proportional lengths of the diagonal through BP-preferred pairings instead of the weighted proportion of contacts within the areas of the BP-preferred pairings. For other forms of BP, other comparable techniques may be applied to adjust gain computations to real-world expectations.

At block 450, an expected gain for the model may be determined based on the BP correction factor applied at block 440. In this way, a model may be favored or discarded over other models to optimize the expected gain over the off cycle pairing strategy (e.g., FIFO, PBR).

After block 450, the BP model evaluation method 400 may end. In some embodiments, the BP model evaluation method 400 may return to block 410 to begin determining alternative BP models to evaluate and compare to other possible BP models, seeking a model with higher or optimal gain.

At this point it should be noted that behavioral pairing model evaluation in a contact center system in accordance with the present disclosure as described above may involve the processing of input data and the generation of output data to some extent. This input data processing and output data generation may be implemented in hardware or software. For example, specific electronic components may be employed in a behavioral pairing model evaluation module or similar or related circuitry for implementing the functions associated with pairing model evaluation in a contact center system in accordance with the present disclosure as described above. Alternatively, one or more processors operating in accordance with instructions may implement the functions associated with BP in a contact center system in accordance with the present disclosure as described above. If such is the case, it is within the scope of the present disclosure that such instructions may be stored on one or more non-transitory processor readable storage media (e.g., a magnetic disk or other storage medium), or transmitted to one or more processors via one or more signals embodied in one or more carrier waves.

The present disclosure is not to be limited in scope by the specific embodiments described herein. Indeed, other various embodiments of and modifications to the present disclosure, in addition to those described herein, will be apparent to those of ordinary skill in the art from the foregoing description and accompanying drawings. Thus, such other embodiments and modifications are intended to fall within the scope of the present disclosure. Further, although the present disclosure has been described herein in the context of at least one particular implementation in at least one particular environment for at least one particular purpose, those of ordinary skill in the art will recognize that its usefulness is not limited thereto and that the present disclosure may be beneficially implemented in any number of environments for any number of purposes. Accordingly, the claims set forth below should be construed in view of the full breadth and spirit of the present disclosure as described herein. 

The invention claimed is:
 1. A method for pairing model evaluation in a contact center system comprising: analyzing, by at least one computer processor communicatively coupled to and configured to operate in the contact center system, historical contactagent outcome data to construct a first pairing model for a plurality of agents and a first plurality of contact types; analyzing, by the at least one computer processor, the historical contactagent outcome data to construct a second pairing model for the plurality of agents and a second plurality of contact types; comparing, by the at least one computer processor, a first expected performance of the contact center system using the first pairing model with a second expected performance of the contact center system using the second pairing model; selecting, by the at least one computer processor, a higher-performing pairing model of the first and second pairing models for use in the contact center system; and selecting, according to the higher-performing pairing model, by the at least one computer processor, at least one agent-contact type pairing for connection in the contact center system to optimize performance of the contact center system.
 2. The method of claim 1, wherein the higher-performing pairing model is a behavioral pairing model.
 3. The method of claim 1, further comprising: applying, by the at least one computer processor and prior to comparing the first expected performance with the second expected performance, a behavioral pairing correction factor to at least one of the first and second pairing models.
 4. The method of claim 1, wherein the pairing model is based on a diagonal behavioral pairing strategy.
 5. The method of claim 1, further comprising: determining, by the at least one computer processor, a historical performance of the contact center system; and determining, by the at least one computer processor, an expected gain in performance of the contact center system using the higher-performing pairing model compared with the historical performance.
 6. The method of claim 1, further comprising: analyzing, by the at least one computer processor, new contact-agent outcome data to update the higher-performing pairing model.
 7. The method of claim 1, further comprising: analyzing, by the at least one computer processor, new contact-agent outcome data to update the first and second pairing models; comparing, by the at least one computer processor, an updated first expected performance of the contact center system using the updated first pairing model with an updated second performance of the contact center system using the updated second pairing model; and selecting, by the at least one computer processor, an updated higher-performing pairing model of the updated first and second pairing models for use in the contact center system.
 8. A system for pairing model evaluation in a contact center system comprising: at least one computer processor communicatively coupled to and configured to operate in the contact center system, wherein the at least one computer processor is further configured to: analyze historical contact-agent outcome data to construct a first pairing model for a plurality of agents and a first plurality of contact types; analyze the historical contact-agent outcome data to construct a second pairing model for the plurality of agents and a second plurality of contact types; compare a first expected performance of the contact center system using the first pairing model with a second expected performance of the contact center system using the second pairing model; select a higher-performing pairing model of the first and second pairing models for use in the contact center system; and select, according to the higher-performing pairing model, at least one agent-contact type pairing for connection in the contact center system to optimize performance of the contact center system.
 9. The system of claim 8, wherein the higher-performing pairing model is a behavioral pairing model.
 10. The system of claim 8, wherein the at least one computer processor is further configured to: apply, prior to comparing the first expected performance with the second expected performance, a behavioral pairing correction factor to at least one of the first and second pairing models.
 11. The system of claim 8, wherein the pairing model is based on a diagonal behavioral pairing strategy.
 12. The system of claim 8, wherein the at least one computer processor is further configured to: determine a historical performance of the contact center system; and determine an expected gain in performance of the contact center system using the higher-performing pairing model compared with the historical performance.
 13. The system of claim 8, wherein the at least one computer processor is further configured to: analyze new contact-agent outcome data to update the higher-performing pairing model.
 14. The system of claim 8, wherein the at least one computer processor is further configured to: analyze new contact-agent outcome data to update the first and second pairing models; compare an updated first expected performance of the contact center system using the updated first pairing model with an updated second performance of the contact center system using the updated second pairing model; and select an updated higher-performing pairing model of the updated first and second pairing models for use in the contact center system.
 15. An article of manufacture for pairing model evaluation in a contact center system comprising: a non-transitory processor readable medium; and instructions stored on the medium; wherein the instructions are configured to be readable from the medium by at least one computer processor communicatively coupled to and configured to operate in the contact center system and thereby cause the at least one computer processor to operate so as to: analyze historical contact-agent outcome data to construct a first pairing model for a plurality of agents and a first plurality of contact types; analyze the historical contact-agent outcome data to construct a second pairing model for the plurality of agents and a second plurality of contact types; compare a first expected performance of the contact center system using the first pairing model with a second expected performance of the contact center system using the second pairing model; select a higher-performing pairing model of the first and second pairing models for use in the contact center system; and select, according to the higher-performing pairing model, at least one agent-contact type pairing for connection in the contact center system to optimize performance of the contact center system.
 16. The article of manufacture of claim 15, wherein the higher-performing pairing model is a behavioral pairing model.
 17. The article of manufacture of claim 15, wherein the at least one computer processor is further caused to operate so as to: apply, prior to comparing the first expected performance with the second expected performance, a behavioral pairing correction factor to at least one of the first and second pairing models.
 18. The article of manufacture of claim 15, wherein the pairing model is based on a diagonal behavioral pairing strategy.
 19. The article of manufacture of claim 15, wherein the at least one computer processor is further caused to operate so as to: determine a historical performance of the contact center system; and determine an expected gain in performance of the contact center system using the higher-performing pairing model compared with the historical performance.
 20. The article of manufacture of claim 15, wherein the at least one computer processor is further caused to operate so as to: analyze new contact-agent outcome data to update the higher-performing pairing model. 